AI in Finance Summit New York schedule

•    How AI/ML is leveraged to enhance customer interactions in financial services through personalized recommendations, AI-driven chatbots, and predictive analytics, creating more engaging and efficient customer experiences?
•    Exploring how AI/ML technologies are improving the financial product cycle and enabling the creation of new financial products and services
•    What key factors must be balanced to ensure the successful and responsible implementation of AI?

 

 

Motivated by persistent pressures in counter-FinCrime, the session will address synthetic dataset generation typologies and their efficacy in fraud and money laundering use cases, as an area of growing interest. Boosted by new Generative AI capabilities, the option for tailoring datasets to desired use cases for model training, opens new avenues for enhanced coverage of possible fraud landscapes. Simulated datasets promise to overcome the ‘Collective Intelligence conundrum’, that is, the inability to share transaction data for knowledge discovery across networks, on account of PII protection and privacy constraints. The questions now become more acute as to (i) how useful synthetic data can be to train models, as measured for utility, privacy and fidelity, and (ii) what are the limits between generative capability and data representativeness - and how far we can and should go with simulated data.

Data has long been a cornerstone of competitive advantage in the financial services industry. Today, as firms navigate the fine line between breakthrough technological advancements, such as AI, and the complexities of regulatory compliance, the need for innovative, data-driven strategies has never been greater. Balancing cutting-edge technology with regulatory demands is essential to unlocking new opportunities and driving meaningful outcomes. This session explores industry-leading performance for HPC and AI workloads, highlighting their transformative impact on banking. It delves into how high-performance data platforms are becoming the secret weapon for gaining a competitive edge, enabling faster insights, more intelligent decision-making, and more agile financial strategies.

Key talking points:
•    Transformative Impact of HPC & AI in Banking – Exploring how industry-leading performance is revolutionizing financial services.
•    High-Performance Data Platforms as a Competitive Advantage – Unlocking faster insights, smarter decision-making, and agile financial strategies.
•    The Future of Finance with AI & HPC – Leveraging advanced technology to stay ahead in an increasingly data-driven industry.

 

•    What is the next chapter in the usage of Data in AI?    
•    What is the next chapter in AI with respect to the framework of models, computational infrastructure, and social implications?
•    How do these new trends in Data and AI improve customer experience, risk management, and operational efficiency in the financial services industry?

Panelists:
John Chan, Director of Technology - AI/ML, Raymond James
Karamjit Singh, VP of Artificial Intelligence, Mastercard
Harry Mendell, Data Architect Technology Group, Federal Reserve Bank of New York
Pravin Kumar, RPA & AI Expert, First Horizon Bank

Moderator: Snehit Cherian, VP, CTO, Global Nexis Solutions, LexisNexis

•    Why financial operational processes are uniquely suited to agent-based automation
•    Core agent capabilities that align with operational workflow challenges 
•    Applied patterns for agentic use cases 

 

•    What are the key challenges financial institutions face when transitioning from AI hype to practical implementation, and how can they overcome them?
•    How can banks ensure that AI solutions are aligned with both regulatory requirements and business goals to deliver real value?
•    What role does data quality and governance play in the successful adoption of AI in financial services, and what steps should organizations take to improve it?
•    How can financial institutions manage the risks associated with AI, such as biases in algorithms and cybersecurity threats, while still maximizing the technology’s potential?

Panelists:
Maddie Daianu, Head of Data & AI, Intuit Credit Karma
Marsal Gavalda, Chief Technology Officer, Clarity AI

Moderator: Georgios Samakovitis, Professor of FinTech, Greenwich University

Legacy systems and tech debt can slow innovation, making modernization feel like an uphill battle. This session cuts through the noise to show how AI can streamline the process, reducing months of manual effort into days. We’ll tackle a common question: "What does my code even do?" and explore how AI accelerates version upgrades, testing, and refactoring. Finally, we’ll showcase a live demo of AI mapping a spaghetti codebase—bringing clarity to complexity. Whether you're facing modernization challenges or just curious about AI's role, this talk offers practical insights for a smarter, faster approach to software evolution.

Stride

•    Highlighting high-performance, energy-efficient solutions with tailored end-to-end support, ideal for financial services like trading and fraud detection
•    Blackwell launch - NVIDIA HGX B200 8-GPU System: Showcase the air-cooled NVIDIA HGX B200, delivering scalable, cost-effective AI performance for financial workloads
•    Demonstrate how Supermicro’s systems reduce latency and enhance efficiency in high-frequency trading and fraud detection

Financial institutions are increasingly implementing AI and machine learning to automate document extraction, classification, and analysis. While traditional OCR (Optical Character Recognition) was a foundational technology, today's solutions far exceed these limited capabilities, moving toward true cognitive automation.

Explore how regulated industries can embrace agentic AI across the SDLC—securely and responsibly—and hear practical, actionable examples of outcomes enabled by a single-platform approach tailored to the needs of financial services institutions

Traditional structural challenges in M&A processes (due diligence limitations, information asymmetry, decision biases)
•    Innovation through AI agents: Automated analysis of contracts and financial data, integration of market volatility factors
•    Concrete examples of value creation: Enhanced decision accuracy, optimized negotiation strategies, reduced timeframes
•    Practical frameworks for implementation and ROI measurement
•    Strategies for improving organizational adoption and effective deployment

•    How can we ensure that AI models used in banking are fair, unbiased, and ethically sound?
•    How can we make AI models more transparent and explainable to both internal stakeholders and customers?
•    What are the best practices for communicating the decision-making process of AI systems?
•    How can banks navigate the evolving regulatory landscape for AI and machine learning?

Asset management firms have widely adopted or experimented with Generative AI (GenAI) powered initiatives across the front, middle, and back office. As those GenAI initiatives have matured, the need for adoption of multi-agentic frameworks has become apparent. 

However, deploying GenAI agents in the absence of a commercially driven data strategy leads to complex, fragmented tech stacks, development inefficiencies, and data security concerns. While firms have successfully prototyped sophisticated agents, the need for greater agility and faster time-to-market has driven many to seek consolidation in their technology infrastructures and data strategies as a necessary precondition. In short, even LLM-driven agents can’t solve a disorganized technology and data strategy. At Snowflake, we are developing state-of-the-art agentic capabilities and orchestration tools, but only as an extension of the data foundation we have laid across the financial services industry. In this talk, we will explore the first principles of data strategy, from ingestion to governance to AI-readiness, and how they are inextricably linked to a commercially successful multi-agentic AI deployment..

John Heisler, Senior AI/ML Architect leading GenAI implementation for financial services, will demonstrate how Snowflake provides a unified, secure, and governed platform for both technology consolidation and multi-agentic frameworks.

In this keynote, we’ll explore how AI is transforming fraud prevention in the financial and fintech industries. We'll discuss the evolution of fraud threats, traditional methods, and how AI is enhancing fraud detection. While AI is revolutionizing the field, it also introduces challenges like bias and ethical concerns. By the end of the session, you'll gain insights into how AI is reshaping fraud prevention, its opportunities, and the steps businesses must take to stay ahead of emerging threats.

•    What are some of the unique challenges financial institutions face when building an AI strategy tailored to their specific needs, and how can they overcome them?
•    How can organizations effectively integrate AI into their existing legacy systems and business processes without causing major disruptions?
•    When it comes to measuring success, what are the most meaningful KPIs for tracking AI adoption in financial services?
•    Can you share any real-world examples where customizing AI for financial services has led to a significant competitive advantage or operational improvement?

Panelists:
Tyler Frieling, Director Emerging Technologies, Blackrock
Haonan Wang, Principal Machine Learning Engineer, GoFi
Miranda Jones, SVP, Data & AI Strategy Leader
Emprise Bank

Modreator: Mark Zabezhinsky, Director, Product Marketing, Alteryx

•    Overcoming data security, fraud detection, and scalability in AI-powered payment solutions
•    How AI is driving innovation in real-time payments, personalized customer experiences, and cost optimization
•    Exploring the role of AI in shaping the next generation of payment systems and industry standards

•    In what specific ways are AI and ML currently reshaping traditional financial products like loans, insurance policies, or investment portfolios? Can you share a practical use case from your experience?
•    How are financial institutions leveraging AI and ML not just to optimize operations, but to unlock entirely new sources of revenue or business models?
•    As we look ahead, which emerging AI/ML technologies do you believe will play a central role in the next wave of innovation in financial products?
•    What lessons have you learned—either through success or failure—when it comes to deploying AI/ML at scale in product development or customer-facing applications?

Panelists:
Akhil Khunger, VP Quantitative Analytics, Barclays
Kristi Kunworee Baishya, AI Product Lead, Nomura Holding America
Frederic Repond, Vice President, Strategy & Innovation, Prudential

Moderator: Drew Cukor, Chief Data & Analytics Officer, TWG Global

•    Prepare and manage trusted data to unlock the full potential of AI in business operations.
•    Focus on the most valuable AI opportunities that deliver measurable results.
•    Develop skilled teams and robust infrastructures to scale AI across the organization.
•    Embed fairness, explainability, and ethics into AI systems to maintain trust and accountability

 

 

•    The evolution of AI agents in finance: From efficiency to revenue generation
•    Quantitative results from collaborations with leading banks and financial institutions
•    Enhanced accuracy and time reduction in M&A strategy, financial analysis, and credit assessment
•    Practical implementation approaches tailored to New York market requirements

•    Unlocking Human Potential in the AI Era
•    Repositioning Talent to Drive Business Outcomes
•    Culture as the Foundation of Innovation

Discover groundbreaking AI technologies aiming to transform finance

•    Leverage accurate, enriched, and analyzed data to unlock your AI investments
•    Verato + Agentic AI ensures identity verification and data validation for exceptional customer experiences
•    Cohort Analyzer enables organizations to create, analyze, and extract enriched data cohorts for revenue growth

•    How are tech stack decisions changing with AI in finance?
•    Where are companies seeing the biggest AI infrastructure challenges?
•    What are the challenges in operationalizing data for AI?
•    Key steps towards addressing and improving return on investment

Panelists:
Richa Singh, Director and Head of Data and AI, Lexington Partners 
Aishwarya Ashok, Senior Data Scientist, PURE Insurance
Dyuti Dave, Technology Analyst, Barclays PLC
Tomasz Noetzel, Senior Equity Analyst, Bloomberg Intelligence

Moderator: Kara Marin, Regional Sales Director, East, Glean

Use cases in Anti Money Laundering, Financial Crimes Compliance
User case in managing Regulatory Development

With the rapid rise of Generative AI (GAI), artificial intelligence has taken center stage for both businesses and regulators alike. As regulations proliferate, staying informed about AI legislative and policy developments across various jurisdictions has become critical. 
Ideally, regulating AI should unlock its potential in a clear, risk-informed manner. But do these regulations achieve that goal? Or do they risk stifling innovation and hindering growth instead?

•    What are the key challenges banks face in keeping up with the rapidly evolving landscape of AI regulations?
•    How can Large Language Models (LLMs) be leveraged to streamline the process of comparing AI regulations?
•    What are the specific benefits of using LLMs for regulatory compliance, particularly in terms of efficiency, accuracy, and insights gained?


 

•    Identifying how LLMs are transforming the analysis of unstructured financial data
•    How LLMs are enabling the development of innovative fintech product
•    Reviewing the current NLP trends and latest SOTA algorithms Overview of the latest NLP algorithms and industry use cases that are easier to solve using the open-source NLP methods
 

•    Understanding the Practice: How third-party financial service providers can access consumer banking transactions and other financial data from banks and non-banks through APIs
•    AI-Powered Use Cases: Exploring real-world use cases of AI in Open Banking
•    Challenges in AI Implementation: Key obstacles and considerations for integrating AI within Open Banking